Reward shaping methods have some parameters that are tuned before starting the training phase. Similar to parametric state and action representation methods, these hyper-parameters settings highly influence the total reward. Tuning the hyper-parameters is challenging and time-consuming which makes it an interesting research direction.
Another interesting research direction is about the effects of intrinsic rewards whether they help in solving the problem of sparse rewards [1]}. For example giving a reward when the agent has a lot of options available, or when the agent encounters a new situation.
